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Purpose: To assess the inter-reader and intra-reader agreement of the Prostate imaging quality version 2 (PI-QUAL v.2) for multiparametric magnetic resonance imaging (mpMRI) among radiologists with varying levels of expertise.
Methods: Fifty men underwent 3 T mpMRI scans in a tertiary referral center. Images were anonymized and assessed by six readers of different expertise (2 expert, 2 basic and 2 beginners) in two sessions: first using PI-QUAL v.2, and then using both PI-QUAL v.2 and v.1 after a 2-week interval. PI-QUAL v.2 scores were considered overall and, for comparison with PI-QUAL v.1, dichotomized according to the threshold of acceptable image quality. Gwet AC index was used to calculate the inter-reader and intra-reader agreement of the scores.
Results: The inter-reader agreement for PI-QUAL v.2 scores was overall moderate (Gwet's AC = 0.55), being higher for expert readers compared to the beginner and basic ones (Gwet's AC = 0.66 versus 0.45-0-58). Intra-reader agreement varied from moderate to perfect (Gwet's AC = 0.43-1.00) and improved with increasing levels of expertise. The ratings were more reproducible for DWI and DCE sequences (Gwet's AC = 0.62-1.00) compared to T2w (Gwet's AC = 0.24-0.70). The intra-reader agreement between PI-QUAL v.2 and v.1 scores across readings ranged from almost perfect to perfect (Gwet's AC = 0.96-1.00).
Conclusions: In a tertiary referral center context, PI-QUAL v.2 is a moderately reliable tool for standardizing prostate mpMRI quality evaluations among readers with varying expertise.
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http://dx.doi.org/10.1016/j.ejrad.2024.111716 | DOI Listing |
Investig Clin Urol
September 2025
Department of Urology, Pusan National University School of Medicine, Yangsan, Korea.
Purpose: This study evaluated inter-/intra-reader agreement with the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 to improve the detection rate of prostate cancer.
Materials And Methods: We enrolled 210 patients who underwent multiparametric magnetic resonance imaging (mpMRI) for clinically suspected or diagnosed prostate cancer.
Osteoarthr Imaging
June 2025
Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA.
Objective: We propose a supplement to MOAKS (MRI Osteoarthritis Knee Score) for capturing >50% partial thickness cartilage loss on knee MRI and measure reader agreement.
Design: MOAKS scores 2 severity levels of cartilage damage (any loss, full-thickness loss) within knee subregions with lesional area graded 0-3. We propose supplemented MOAKS (sMOAKS) by adding a similarly graded third level assessment for deep cartilage loss (DCL), >50% thickness, in addition to traditional MOAKS for improved granularity of partial thickness cartilage loss.
J Clin Med
August 2025
Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada.
Identifying high-grade strictures (HGS) in patients with primary sclerosing cholangitis (PSC) relies upon subjective assessments of magnetic resonance cholangiopancreatography (MRCP). Quantitative MRCP (MRCP+) provides objective evaluation of MRCP examinations, which may help make these assessments more consistent and improve patient management and selection for intervention. We evaluated the impact of MRCP+ on clinicians' confidence in diagnosing HGS in patients with PSC.
View Article and Find Full Text PDFNPJ Digit Med
July 2025
Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Three-dimensional magnetic resonance vessel wall imaging (3D MR-VWI) is critical for characterizing cerebrovascular pathologies, yet its clinical adoption is hindered by labor-intensive postprocessing. We developed VWI Assistant, a multi-sequence integrated deep learning platform trained on multicenter data (study cohorts 1981 patients and imaging datasets) to automate artery segmentation and reconstruction. The framework demonstrated robust performance across diverse patient populations, imaging protocols, and scanner manufacturers, achieving 92.
View Article and Find Full Text PDFJ Appl Clin Med Phys
August 2025
Division of Pulmonary, Critical Care and Sleep Medicine, University of Miami, Miami, Florida, USA.
Background: Current approaches to lung parcellation utilize established fissures between lobes to provide estimates of lobar volume. However, deep learning segment parcellation provides the ability to better assess regional heterogeneity in ventilation and perfusion.
Purpose: We aimed to validate and demonstrate the clinical applicability of CT-based lung segment parcellation using deep learning on a clinical cohort with mixed airways disease.